

import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	DebertaTokenizer
							UpperCAmelCase_						:		str								=	True
							UpperCAmelCase_						:		Optional[int]								=	DebertaTokenizerFast




							def       snake_case			(					self						)		->       int:
														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														A      :					Dict      		=					[
														    '''l''',
														    '''o''',
														    '''w''',
														    '''e''',
														    '''r''',
														    '''s''',
														    '''t''',
														    '''i''',
														    '''d''',
														    '''n''',
														    '''\u0120''',
														    '''\u0120l''',
														    '''\u0120n''',
														    '''\u0120lo''',
														    '''\u0120low''',
														    '''er''',
														    '''\u0120lowest''',
														    '''\u0120newer''',
														    '''\u0120wider''',
														    '''[UNK]''',
														]
														A      :					Tuple      		=					dict(zip(__UpperCAmelCase    ,						range(len(__UpperCAmelCase						)						)						)						)
														A      :					int      		=					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
														A      :					Union[str, Any]      		=					{'''unk_token''': '''[UNK]'''}

														A      :					Optional[Any]      		=					os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES['''vocab_file''']						)
														A      :					Dict      		=					os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES['''merges_file''']						)
														with open(self.vocab_file    ,						'''w'''    ,						encoding='''utf-8'''						) as fp:
																					fp.write(json.dumps(__UpperCAmelCase						) + '''\n'''						)
														with open(self.merges_file    ,						'''w'''    ,						encoding='''utf-8'''						) as fp:
																					fp.write('''\n'''.join(__UpperCAmelCase						)						)




							def       snake_case			(					self    ,						**__UpperCAmelCase						)		->       int:
														kwargs.update(self.special_tokens_map						)
														return self.tokenizer_class.from_pretrained(self.tmpdirname    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       int:
														A      :					Tuple      		=					'''lower newer'''
														A      :					str      		=					'''lower newer'''
														return input_text, output_text




							def       snake_case			(					self						)		->       int:
														A      :					str      		=					self.get_tokenizer()
														A      :					int      		=					'''lower newer'''
														A      :					Tuple      		=					['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
														A      :					List[Any]      		=					tokenizer.tokenize(__UpperCAmelCase						)
														self.assertListEqual(__UpperCAmelCase    ,						__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					tokens + [tokenizer.unk_token]
														A      :					Dict      		=					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase						)    ,						__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Dict:
														A      :					List[str]      		=					self.get_tokenizer()
														A      :					int      		=					tokenizer('''Hello'''    ,						'''World'''						)
														A      :					List[str]      		=					[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
														self.assertListEqual(tokd['''token_type_ids''']    ,						__UpperCAmelCase						)




							@slow
							def       snake_case			(					self						)		->       List[str]:
														A      :					Any      		=					self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''						)

														A      :					Tuple      		=					tokenizer.encode('''sequence builders'''    ,						add_special_tokens=__UpperCAmelCase						)
														A      :					List[str]      		=					tokenizer.encode('''multi-sequence build'''    ,						add_special_tokens=__UpperCAmelCase						)

														A      :					Dict      		=					tokenizer.encode(
														    '''sequence builders'''    ,						add_special_tokens=__UpperCAmelCase    ,						add_prefix_space=__UpperCAmelCase						)
														A      :					List[str]      		=					tokenizer.encode(
														    '''sequence builders'''    ,						'''multi-sequence build'''    ,						add_special_tokens=__UpperCAmelCase    ,						add_prefix_space=__UpperCAmelCase						)

														A      :					List[str]      		=					tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						)
														A      :					Optional[int]      		=					tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase    ,						__UpperCAmelCase						)

														assert encoded_sentence == encoded_text_from_decode
														assert encoded_pair == encoded_pair_from_decode






							@slow
							def       snake_case			(					self						)		->       Optional[int]:
														A      :					Any      		=					[self.tokenizer_class]
														if self.test_rust_tokenizer:
																					tokenizer_classes.append(self.rust_tokenizer_class						)

														for tokenizer_class in tokenizer_classes:
																					A      :					Dict      		=					tokenizer_class.from_pretrained('''microsoft/deberta-base'''						)

																					A      :					Dict      		=					[
																					    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																					    '''ALBERT incorporates two parameter reduction techniques''',
																					    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																					    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																					    ''' vocabulary embedding.''',
																					]

																					A      :					Tuple      		=					tokenizer(__UpperCAmelCase    ,						padding=__UpperCAmelCase						)
																					A      :					int      		=					[tokenizer.decode(__UpperCAmelCase    ,						skip_special_tokens=__UpperCAmelCase						) for seq in encoding['''input_ids''']]

																					# fmt: off
																					A      :					Any      		=					{
																					    '''input_ids''': [
																					        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
																					    ],
																					    '''token_type_ids''': [
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																					    ],
																					    '''attention_mask''': [
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																					    ]
																					}
																					# fmt: on

																					A      :					List[str]      		=					[
																					    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																					    '''ALBERT incorporates two parameter reduction techniques''',
																					    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																					    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																					    ''' vocabulary embedding.''',
																					]

																					self.assertDictEqual(encoding.data    ,						__UpperCAmelCase						)

																					for expected, decoded in zip(__UpperCAmelCase    ,						__UpperCAmelCase						):
																												self.assertEqual(__UpperCAmelCase    ,						__UpperCAmelCase						)









from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_ = "x"     ,				lowerCamelCase_ = 10**-10     ,				lowerCamelCase_ = 1     ,				):
							A      :					int      		=					symbols(lowerCamelCase_		)
							A      :					List[str]      		=					lambdify(lowerCamelCase_     ,				lowerCamelCase_		)
							A      :					Tuple      		=					lambdify(lowerCamelCase_     ,				diff(lowerCamelCase_     ,				lowerCamelCase_		)		)

							A      :					Optional[int]      		=					starting_point

							while True:
														if diff_function(lowerCamelCase_		) != 0:
																					A      :					Union[str, Any]      		=					prev_guess - multiplicity * func(lowerCamelCase_		) / diff_function(
																					    lowerCamelCase_		)
														else:
																					raise ZeroDivisionError('''Could not find root'''		) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess		) < precision:
																					return next_guess

														A      :					Tuple      		=					next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}",
		)

		# Find root of cos(x)
		print(F"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")






